基于事件的分布式合作神经学习控制,用于具有时间变化的输出约束的非线性多代理系统
Congyan Lv1, Guangliang Liu1, Yingnan Pan1
1School of Control Science and Engineering, Bohai University, Jinzhou 121013, Liaoning, China.
概括
本研究介绍了非线性多元代理系统的新控制策略,确保在输出约束下性能,并减少通信负载. 该方法保证了系统稳定性和约束满足,提高了运营安全性.
科学领域:
- 控制系统工程 控制系统工程
- 人工智能的人工智能
- 网络化系统 网络化系统
背景情况:
- 实用工程系统经常面临性能下降,原因是安全限制和来自代理间通信的网络负担.
- 现有的控制方法在非线性多元代理系统中扎着随时间变化的输出约束和通信低效率.
研究的目的:
- 为具有时间变化的输出约束的非线性多代理系统开发分布式合作学习控制策略.
- 通过自适应控制机制增强系统安全并减少通信资源的使用.
主要方法:
- 一个改进的输出依赖的通用屏障功能被设计用于处理可调的对称或不对称输出约束.
- 基于神经网络 (NN) 重量的切换事件触发条件被开发用于自适应频率调整.
- 使用帕德近似技术来管理输入延迟并简化控制器设计.
- 利亚普诺夫稳定理论被应用来证明系统的收和边界性.
主要成果:
- 拟议的控制策略确保追随者的输出接近领导者的输出,同时尊重输出约束.
- 封闭循环系统中的所有信号都被证明最终仍然有界限.
- 事件触发条件可自适应地调整NN权重更新,优化通信资源利用率.
结论:
- 提出的方法有效地解决了具有时间变化的输出约束和通信负担的非线性多代理系统.
- 开发的控制策略增强了通过模拟结果验证的系统安全性和稳定性.
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